Provably Rare Gem Miner.

Related tags

Deep LearningProvably-Rare-Gem-Miner
Overview

Provably Rare Gem Miner

just another random project by yoyoismee.eth

useful link

useful thing you should know

  • read contract -> gems(gemID) to get useful info
  • write contract -> mine to claim(kind, salt) to claim your NFT

to run. just edit the python file and run it.

pip install -r requirement.txt
python3 stick_the_miner.py

or new one auto_mine.py for less input. but you'll need infura account

Ps. too lazy to write docs. but it's 50 LoCs have fun.

why stick the miner ? welp.. this is part of the stick the BUIDLer series.

TL;DR - I'm working on a series of opensource NFT related project just for fun.

Key parameters to change if you are using orginal version 'stick_the_miner.py' (cr. K Nattakit's FB post)

  • chain_id - eth:1, fantom:250
  • entropy - ??
  • gemAddr - Game address, can get from https://gems.alphafinance.io/ (loot/bloot/rarity)
  • userAddr - your Wallet address
  • kind = ประเภทของเพชรที่จะขุด ผมแนะนำเป็น Emerald เพราะ return/difficult สูงที่สุด ง่าย ๆ คือคุณจะกำไรเร็วกว่านั่นเอง
  • nonce - number of times you've minted a gem (https://gems.alphafinance.io/ and connect your wallet)
  • diff - difficulty of gemID (https://gems.alphafinance.io/), note that this changes everytime someone minted that gem, so you need to change it too

(more detail) how to use 'auto_mine.py', the updated version of stick_the_miner

  • benefits: manual version (stick_the_miner.py) requires you to update the 'diff' parameter every time someone minted the nft of the target gem, and 'nounce' if you successfully minted one. This version automates that so you just have to rerun to update.
  • steps:
    1. update requirements pip install -r requirements.txt
    1. create an account at (https://infura.io/), select your chain (e.g. Ethereum), create a project and obtain your project ID
    1. create a .env file in the same format as .env-example, inputing your information from (2.), your wallet address and gem ID
    1. python3 auto_mine.py
  • Note: although you dont have to manually adjust 'diff' parameter everytime, you still need to restart the process everytime someone minted target gem's nft still

Once you get the salt:

Multicore version

  • Normal version uses only 1 core of processors, the multicore version should be ~8 times faster depending on your CPU / coreNumber variable
  • You can select the number of processors by chainging coreNumber variable (should not exceed ~16 tho)
  • "fantom_mining_pool_auto_multicore_line.py" is the multicore version of fantom_mining_pool.py
  • for mining by yourself and manual claim please use "fantom_multicore_line.py"
Comments
  • 🎨Added colorlog package for output with colors

    🎨Added colorlog package for output with colors

    I use the classic stick_the_miner.py for mining and had a hard time looking for the salt output due to the monochrome color. So, I decided to differentiate the salt output with the colorlog package😁

    opened by mickyngub 2
  • Multicore version of the miner for both pool mining and self mining

    Multicore version of the miner for both pool mining and self mining

    Depending on your CPU and the coreNumber variable, it should be ~8 times faster than the original version but with the drawback of a tremendous increase in CPU utilization.

    opened by mickyngub 1
  • Lowering the priority of python.exe to reduce lags

    Lowering the priority of python.exe to reduce lags

    If a user is mining gems in the background while using other compute-intensive programs, the user might experience lags due to 100% CPU utilization. By lowering the priority of python.exe miner, other programs will have higher priorities. Thus, users would be less likely to experience lagging issues.

    Under a normal circumstance in which the CPU utilization is less than 100%, it should have no impact on iter/sec.

    Before

    image

    After

    image

    opened by mickyngub 1
  • update fantom_mining_pool

    update fantom_mining_pool

    • edit .env-example add NOTIFY_AUTH_TOKEN, DIFF and PRIVATE_KEY
    • edit var private_key to PRIVATE_KEY
    • insert if PRIVATE_KEY != ''
    • get PRIVATE_KEY from .env for safety
    opened by NuttakitDW 0
  • why other people mint so quickly

    why other people mint so quickly

    https://ftmscan.com/address/0x729d74098f6669541ed1b69403ae75f080ccf1e1

    this people mint level 4 gems so quickly ，his salt is too low, but execute success.

    are you knonw the reason? image

    opened by sumrise 3
  • refactor to support multiple chain properly

    refactor to support multiple chain properly

    some of our code is unnecessary based on Ethereum e.g. infura_key, hard code chain no, and more todo: refactor to a more generic one that would be valid across all EVM compatible chain e.g. infura_key -> rpc_provider (also fix others code to match this change) and more

    also TODO: remove the quick fix for fantom file LOL

    opened by yoyoismee 0
  • Idea for sampling different range of int random on multiple workers

    Idea for sampling different range of int random on multiple workers

    Will probably do tmr, parse n worker to the get_salt function so each worker could random int from different range of numbers eg. worker 1: 1-2^122, worker 2: 2^122 to 2^123

    opened by Duayt 1
Releases(v0.0.1d-test-build)
Owner
GitHub Repository
This repository provides a basic implementation of our GCPR 2021 paper "Learning Conditional Invariance through Cycle Consistency"

Learning Conditional Invariance through Cycle Consistency This repository provides a basic TensorFlow 1 implementation of the proposed model in our GC

BMDA - University of Basel 1 Nov 04, 2022
Liver segmentation using MONAI and pytorch

Machine Learning use case in the field of Healthcare. In this project MONAI and pytorch frameworks are used for 3D Liver segmentation.

Abhishek Gajbhiye 2 May 30, 2022
Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite.

TFLite-HITNET-Stereo-depth-estimation Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite. Stereo depth e

Ibai Gorordo 22 Oct 20, 2022
TLDR: Twin Learning for Dimensionality Reduction

TLDR (Twin Learning for Dimensionality Reduction) is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self

NAVER 105 Dec 28, 2022
Zero-shot Learning by Generating Task-specific Adapters

Code for "Zero-shot Learning by Generating Task-specific Adapters" This is the repository containing code for "Zero-shot Learning by Generating Task-s

INK Lab @ USC 11 Dec 17, 2021
Tooling for GANs in TensorFlow

TensorFlow-GAN (TF-GAN) TF-GAN is a lightweight library for training and evaluating Generative Adversarial Networks (GANs). Can be installed with pip

803 Dec 24, 2022
DIR-GNN - Discovering Invariant Rationales for Graph Neural Networks

DIR-GNN "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)

Ying-Xin (Shirley) Wu 70 Nov 13, 2022
Implementation of CVPR 2021 paper "Spatially-invariant Style-codes Controlled Makeup Transfer"

SCGAN Implementation of CVPR 2021 paper "Spatially-invariant Style-codes Controlled Makeup Transfer" Prepare The pre-trained model is avaiable at http

118 Dec 12, 2022
NEG loss implemented in pytorch

Pytorch Negative Sampling Loss Negative Sampling Loss implemented in PyTorch. Usage neg_loss = NEG_loss(num_classes, embedding_size) optimizer =

Daniil Gavrilov 123 Sep 13, 2022
'Aligned mixture of latent dynamical systems' (amLDS) for stimulus decoding probabilistic manifold alignment across animals. P. Herrero-Vidal et al. NeurIPS 2021 code.

Across-animal odor decoding by probabilistic manifold alignment (NeurIPS 2021) This repository is the official implementation of aligned mixture of la

Pedro Herrero-Vidal 3 Jul 12, 2022
Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al.

nam-pytorch Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al. [abs, pdf] Installation You can access nam-pytorch vi

Rishabh Anand 11 Mar 14, 2022
Patch2Pix: Epipolar-Guided Pixel-Level Correspondences [CVPR2021]

Patch2Pix for Accurate Image Correspondence Estimation This repository contains the Pytorch implementation of our paper accepted at CVPR2021: Patch2Pi

Qunjie Zhou 199 Nov 29, 2022
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

pytorch-a2c-ppo-acktr Update (April 12th, 2021) PPO is great, but Soft Actor Critic can be better for many continuous control tasks. Please check out

Ilya Kostrikov 3k Jan 09, 2023
Flexible time series feature extraction & processing

tsflex is a toolkit for flexible time series processing & feature extraction, that is efficient and makes few assumptions about sequence data. Useful

PreDiCT.IDLab 206 Dec 28, 2022
Dense Gaussian Processes for Few-Shot Segmentation

DGPNet - Dense Gaussian Processes for Few-Shot Segmentation Welcome to the public repository for DGPNet. The paper is available at arxiv: https://arxi

37 Jan 07, 2023
converts nominal survey data into a numerical value based on a dictionary lookup.

SWAP RATE Converts nominal survey data into a numerical values based on a dictionary lookup. It allows the user to switch nominal scale data from text

Jake Rhodes 1 Jan 18, 2022
AntroPy: entropy and complexity of (EEG) time-series in Python

AntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to e

Raphael Vallat 153 Dec 27, 2022
Official implementation for ICDAR 2021 paper "Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer"

Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer Description Convert offline handwritten mathematical expressi

Wenqi Zhao 87 Dec 27, 2022
Repo for FUZE project. I will also publish some Linux kernel LPE exploits for various real world kernel vulnerabilities here. the samples are uploaded for education purposes for red and blue teams.

Linux_kernel_exploits Some Linux kernel exploits for various real world kernel vulnerabilities here. More exploits are yet to come. This repo contains

Wei Wu 472 Dec 21, 2022
这是一个yolox-keras的源码，可以用于训练自己的模型。

YOLOX：You Only Look Once目标检测模型在Keras当中的实现 目录 性能情况 Performance 实现的内容 Achievement 所需环境 Environment 小技巧的设置 TricksSet 文件下载 Download 训练步骤 How2train 预测步骤 Ho

Bubbliiiing 64 Nov 10, 2022